About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
Wireless Mobile Communication and Healthcare. 12th EAI International Conference, MobiHealth 2023, Vila Real, Portugal, November 29-30, 2023 Proceedings

Research Article

A Vision Transformer Approach to Fundus Image Classification

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-60665-6_11,
        author={Danilo Leite and Jos\^{e} Camara and Jo\"{a}o Rodrigues and Ant\^{o}nio Cunha},
        title={A Vision Transformer Approach to Fundus Image Classification},
        proceedings={Wireless Mobile Communication and Healthcare. 12th EAI International Conference, MobiHealth 2023, Vila Real, Portugal, November 29-30, 2023 Proceedings},
        proceedings_a={MOBIHEALTH},
        year={2024},
        month={6},
        keywords={Fundus Image Vision transformers BRSET},
        doi={10.1007/978-3-031-60665-6_11}
    }
    
  • Danilo Leite
    José Camara
    João Rodrigues
    António Cunha
    Year: 2024
    A Vision Transformer Approach to Fundus Image Classification
    MOBIHEALTH
    Springer
    DOI: 10.1007/978-3-031-60665-6_11
Danilo Leite, José Camara, João Rodrigues1, António Cunha,*
  • 1: LARSyS & ISE, Universidade do Algarve
*Contact email: acunha@utad.pt

Abstract

Glaucoma is a condition that affects the optic nerve, with loss of retinal nerve fibers, increased excavation of the optic nerve, and a progressive decrease in the visual field. It is the leading cause of irreversible blindness in the world. Manual classification of glaucoma is a complex and time-consuming process that requires assessing a variety of ocular features by experienced clinicians. Automated detection can assist the specialist in early diagnosis and effective treatment of glaucoma and prevent vision loss. This study developed a deep learning model based on vision transformers, called ViT-BRSET, to detect patients with increased excavation of the optic nerve automatically. ViT-BRSET is a neural network architecture that is particularly effective for computer vision tasks. The results of this study were promising, with an accuracy of 0.94, an F1-score of 0.91, and a recall of 0.94. The model was trained on a new dataset called BRSET, which consists of 16,112 fundus images of patients with increased excavation of the optic nerve. The results of this study suggest that ViT-BRSET has the potential to improve early diagnosis through early detection of optic nerve excavation, one of the main signs of glaucomatous disease. ViT-BRSET can be used to mass-screen patients, identifying those who need further examination by a doctor.

Keywords
Fundus Image Vision transformers BRSET
Published
2024-06-28
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-60665-6_11
Copyright © 2023–2025 ICST
EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL